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Month: December 2015

“Deep Learning” is one of the major technologies of artificial intelligence. In April 2013, two and half years ago, MIT technology review selected “Deep Learning” as one of the 10 breakthrough technologies 2013. Since then it has been developed so rapidly that it is not a dream anymore now. This article is the final one in 2015. Therefore, I would like to look back the progress of “Deep Learning” this year and consider how it changes our daily lives in 2016.

How has “Deep Learning” progressed in 2015?

1. “Deep Learning” moves from laboratories to software developers in the real world

In 2014, Major breakthrough of deep learning occurred in the major laboratory of big IT companies and universities. Because it required complex programming and huge computational resources. To do that effectively, massive computational assets and many machine learning researchers were required. But in 2015, many programs, softwares of deep learning jumped out of the laboratory into the real world. Torch, Chainer, H2O and TensorFlow are the examples of them. Anyone can develop apps with these softwares as they are open-source. They are also expected to use in production. For example, H2O can generate the models to POJO (Plain Old Java Code) automatically. This code can be implemented into production system. Therefore, there are fewer barriers between development and production anymore. It will accelerate the development of apps in practice.

2. “Deep Learning” start understanding languages gradually.

Most of people use more than one social network, such as Facebook, Linkedin, twitter and Instagram. There are many text format data in them. They must be treasury if we can understand what they say immediately. In reality, there are too much data for people to read them one by one. Then the question comes. Can computers read text data instead of us? Many top researchers are challenging this area. It is sometimes called “Natural Language Processing“. In short sentences, computers can understand the meaning of sentences now. This app already appeared in the late of 2015. This is “Smart Reply” by Google. It can generate candidates of a reply based on the text in a receiving mail. Behind this app, “LSTM (Long short term memory)” which is one of the deep learning algorithm is used. In 2016, computers might understand longer sentences/paragraphs and answer questions based on their understanding. It means that computers can step closer to us in our daily lives.

3. Cloud services support “Deep Learning” effectively.

Once big data are obtained, infrastructures, such as computational resources, storages, network are needed. If we want to try deep learning, it is better to have fast computational resources, such as Spark. Amazon web services, Microsoft Azure, Google Cloud Platform and IBM Bluemix provide us many services to implement deep learning with scale. Therefore, it is getting much easier to start implementing “Deep Learning” in the system. Most cloud services are “pay as you go” so there is no need to pay the initial front cost to start these services. It is good, especially for small companies and startups as they usually have only limited budgets for infrastructures.

How will “Deep Learning” change our daily lives in 2016?

Based on the development of “Deep learning” in 2015, many consumer apps with “Deep learning” might appear in the market in 2016. The deference between consumer apps with and without “Deep Learning” is ” Apps can behave differently by users and conditions”. For example, you and your colleagues might see a completely different home screen even though you and your colleagues use the same app because “Deep learning” enables the app to optimize itself to maximize customer satisfaction. In apps of retail shops, top pages can be different by customers according to customer preferences. In apps of education, learners can see different contents and questions as they have progressed in the courses. In apps of navigations, the path might be automatically appeared based on your specific schedule, such as the path going airport on the day of the business trip. They are just examples. It can be applied across the industries. In addition to that, it can be more sophisticated and accurate if you continue to use the same app because it can learn your behavior rapidly. It can always be updated to maximize customer satisfactions. It means that we do not need to choose what we want, one by one because computers do that instead of us. Buttons and navigators are less needed in such apps. All you have to do is to input the latest schedules in your computers. Everything can be optimized based on the updated information. People are getting lazy? Maybe yes if apps are getting more sophisticated as expected. It must be good for all of us. We may be free to do what we want!

Actually, I quit an investment bank in Tokyo to set up my start-up at the same time when MIT technology review released 10 breakthrough technologies 2013. Initially I knew the word “Deep Learning” but I could not understand how important is is to us because it was completely new for me. However, I am so confident now that I always say “Deep Learning'” is changing the landscape of jobs, industries and societies. Could you agree with that? I imagine everyone can agree that by the end of 2016!

Notice: TOSHI STATS SDN. BHD. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. TOSHI STATS SDN. BHD. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on TOSHI STATS SDN. BHD. and me to correct any errors or defects in the codes and the software.

I started the group of “big data and digital economy” in Linked in on 15th April this year. Now the participants are over 300 people! This is beyond my initial expectation. So I would like to appreciate all of you for your support.

I prepare several small Chirstmas presents here. If you are interested in, please let me know. I will do my best!

1. Your theme of my weekly letter

As you know, I write the weekly letter “big data and digital economy” every week and publish it in Linkedin. If you are interested in specific themes, I would like to research and write them as long as I can. Anything is OK if it is about digital economy. Please let me know!

2. Applications of data analysis in 2016

In 2016, I would like to develop my applications using data analysis and make them public through the internet. As long as data is “public”, we can do any analysis on the data. Therefore, if you would like to look at your own analysis based on public data, could you let me know what you are interested in? These are examples of applications provided by “shiny”, very famous tool among data scientists.

This is a project of my company in 2016. To support for business personnel to learn R-programming, I would like to set up the platform where participants can learn R-programming interactively with ease. Contents are very important in order for participants to keep learning motivations. When you have specific themes which you want to learn, could you let me know? These themes may be included as programs in the platform going forward! This is an introductory video of the platform.

There are many Christmas trees in shopping malls. It makes us a little happier. Children must expect big presents at Christmas eve. I am also waiting for my presents, although I do not know where my Santa Claus is now.

This is the second time when I live in KL at the time of Christmas. Then I feel a little strange because it is hot at the time of the Christmas season in KL. In Japan, it is cold and it has sometimes massive snow in December. Whenever I saw Christmas trees in Japan, it was always cold. But now it is hot in KL! I think most of Asean countries have no snow so there are few opportunities where we can feel “snow”.

The picture above is taken in KL. On the roof of the house, there is snow. But I do not see snow on the trees. White balls look just decorations for me. It must be OK as there is no snow in KL. On the other hand, this picture below is taken in Japan. There are many symbols of snow on the Christmas trees.

Some of you have been to Hokkaido, the north part of Japan to enjoy snow in winter. The whole land is sometimes covered with “snow” there in winter. So everything looks white and it is very quiet, no sound is heard because noises are absorbed by thick snow on the ground. In a such case, Christmas trees must have “snow” on them. So it may be different, location by location.

I do not have any statistics of ” how many trees have ‘snow” on them in shopping malls all over the world”. But it is interesting for me because it tells us how weather and climate affect our behaviors. Because Japan has four seasons (spring, summer, autumn and winter), predictions of its climate are very important for companies as well. Hotter summer means more sales of juice, ice cream and air conditioners, vice versa. If winter is not so cold than usual, sweaters and coats are not selling well. It means less flu so it is good for children and senior people, but it is not so good for the pharmaceutical industry. In this way, weather and climate have huge impacts to our behavior and economy.

The data about weather and climate may be relatively unused in companies in order to make business decisions so far. But as we have more data about them and obtain predictions with accuracy, it is worthwhile using data about weather and climate in the businesses now. I would like to take examples of analysis about weather and climate going forward.

This is amazing! It is one of the most incredible applications for me this year! I am very excited about that. Let me share with you as you can use it, too.

This is “Smart Reply of Inbox”, an e-mail application from Google. It was announced on 3rd November. I try it today.

For example, I got e-mail from Hiro. He asked me to have a lunch tomorrow. In the screen, three candidates of my answer appear automatically. 1. Yes, what time? 2. Yes, what’s up 3. No, sorry. These candidates are created after computers understand what Hiro said in the e-mail. So each of them is very natural for me.

So all I have to do is just to choose the first candidate and send it to Hiro. It is easy!

I always wonder how computers understand the meaning of words and sentences. In this application, sentences are represented in fixed sized vectors. It means that each sentence is converted to sequences of numbers. If two sentences have the same meaning, the vector of each sentence should be similar to each other even though the original sentences look different.

This technology is one of the machine learning. Therefore, the more people use it, the more sophisticated it can be because it can learn by itself. Now it applies to relatively short sentences like e-mail. But I am sure it will be applied to longer sentences, such as official documents in business. I wonder when it happens in the future. Pro. Geoffrey Hinton is expected to research this area with intense. If it happens, computers will be able to understand what documents mean and create some sentences based on their understanding. I do not know how Industires are changed when it happens.

This kind of technology is sometimes referred as “Natural language processing” or “NLP”. I want to focus on this area as a main research topic of my company in 2016. Some progresses will be shared through my weekly letter here.

I would like to recommend you to try Smart Reply of Inbox and enjoy it! Let me know your impressions. Cheers!

Note: Toshifumi Kuga’s opinions and analyses are personal views and are intended to be for informational purposes and general interest only and should not be construed as individual investment advice or solicitation to buy, sell or hold any security or to adopt any investment strategy. The information in this article is rendered as at publication date and may change without notice and it is not intended as a complete analysis of every material fact regarding any country, region market or investment.

Data from third-party sources may have been used in the preparation of this material and I, Author of the article has not independently verified, validated such data. I and TOSHI STATS.SDN.BHD. accept no liability whatsoever for any loss arising from the use of this information and relies upon the comments, opinions and analyses in the material is at the sole discretion of the user.